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Supervised Learning

Learning from labeled data

Supervised Learning involves training models on labeled datasets to predict outcomes for new, unseen data.

Finding patterns in unlabeled data

Unsupervised Learning identifies hidden structures in data without predefined labels, such as clustering and dimensionality reduction.

Combining labeled and unlabeled data

Semi-Supervised Learning uses a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy.

Generative Pre-trained Transformer

GPT models generate human-like text by predicting the next word in a sequence using transformer architecture.

Ensemble of decision trees

Random Forest builds multiple decision trees and combines their outputs for robust predictions.

Guidelines for responsible AI

AI Ethics addresses fairness, transparency, and accountability in AI systems to prevent harm and bias.

Sequential ensemble method

Gradient Boosting builds models sequentially, correcting errors of previous models for improved accuracy.

Making AI decisions understandable

Explainable AI provides insights into how models make decisions, improving trust and compliance.

Optimized gradient boosting

XGBoost is a high-performance implementation of gradient boosting for structured data.